Main Article Content
The Sharp GP2Y0A02YK0F is categorized as a nonlinear sensor for distance measurement. This sensor is also categorized as a low-cost sensor. The higher resolution, cheap, high accuracy and easy to install are the advantages. The accuracy level of this sensor depends on the type of the measured object materials, requires an additional device unit and further processing is required since the output is non-linear. The distance determination is not easy for this type of sensor since the characteristic of this sensor fulfills non-injective function. The modelling process is one of methods to convert the output voltage of the sensor to a distance unit. The advantages of polynomial modelling are simple form model, moderate in flexibilities of shape, well known and understood properties, and easy to use for computational matters. The obstacle of polynomial-based modelling is the presence of Runge’s phenomenon. The minimization of Runge’s phenomenon can be done with decreasing the model order. The piecewise Newton polynomials with vertex determination method have been succeeded to generate a nonlinear model and minimize the occurrence of Runge’s phenomenon. The low level of MSE by 0.001 and error percentage of 2.38% has been obtained for the generated model. The low MSE level leads to the high accuracy level of the generated model.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
 P. Malheiros, J. Gonçalves, and P. Costa, “Towards a more Accurate Infrared Distance Sensor Model,” Manuf. Syst. Eng. Unit, no. Sept, 2010.
 D. M. Sobers, G. Chowdhary, and E. N. Johnson, “Indoor navigation for Unmanned Aerial Vehicles,” AIAA Guid. Navig. Control Conf. Exhib., no. August, 2009, doi: 10.2514/6.2009-5658.
 B. Mustapha, A. Zayegh, and R. K. Begg, “Ultrasonic and infrared sensors performance in a wireless obstacle detection system,” Proc. - 1st Int. Conf. Artif. Intell. Model. Simulation, AIMS 2013, pp. 487–492, 2014, doi: 10.1109/AIMS.2013.89.
 P. J. C. Biselli, R. S. Nóbrega, and F. G. Soriano, “Nonlinear flow sensor calibration with an accurate syringe,” Sensors (Switzerland), vol. 18, no. 7, pp. 1–9, 2018, doi: 10.3390/s18072163.
 R. Srivastava and P. Srivastava, “Comparison of Lagrange’s and Newton’s interpolating polynomials,” J. Exp. Sci., vol. 3, no. 1, pp. 1–4, 2012, [Online]. Available: http://jexpsciences.com/index.php/jexp/article/viewArticle/12469.
 C. Yiqing, “High-order Polynomial Interpolation Based on the Interpolation Center ’ s Neighborhood the amendment to the Runge phenomenon,” pp. 1–4, 2009, doi: 10.1109/WCSE.2009.295.
 C. Ye, S. Feng, Z. Xue, C. Guo, and Y. Zhang, “Defeating Runge Problem by Coefficients and Order Determination Method with Various Approximation Polynomials,” 2018 37th Chinese Control Conf., pp. 8622–8627, 2018.
 D. Chen, T. Qiao, H. Tan, M. Li, and Y. Zhang, “Solving the problem of Runge phenomenon by pseudoinverse cubic spline,” Proc. - 17th IEEE Int. Conf. Comput. Sci. Eng. CSE 2014, Jointly with 13th IEEE Int. Conf. Ubiquitous Comput. Commun. IUCC 2014, 13th Int. Symp. Pervasive Syst. , pp. 1226–1231, 2015, doi: 10.1109/CSE.2014.237.
 M. Hu and F. Li, “A New Method to Solve Numeric Solution of Nonlinear Dynamic System,” Math. Probl. Eng., vol. 2016, 2016, doi: 10.1155/2016/1485759.
 W. P. Carey and S. S. Yee, “Calibration of nonlinear solid-state sensor arrays using multivariate regression techniques,” Sensors Actuators B. Chem., vol. 9, no. 2, pp. 113–122, 1992, doi: 10.1016/0925-4005(92)80203-A.
 L. Hou, T. He, and Y. Yang, “A virtual instrument for sensors nonlinear errors calibration,” Conf. Rec. - IEEE Instrum. Meas. Technol. Conf., vol. 3, no. May, pp. 2103–2106, 2005.
 X. jun Fan et al., “A method for nonlinearity compensation of OFDR based on polynomial regression algorithm,” Optoelectron. Lett., vol. 16, no. 2, pp. 108–111, 2020, doi: 10.1007/s11801-020-9047-8.
 I. Balk, “Notes on Polynomial Selection for Piecewise,” Electr. Perform. Electr. Packag. (IEEE Cat. No. 03TH8710), pp. 177–180, 2003.
 K. Abuhmaidan, “Bijective, Non-Bijective and Semi-Bijective Translations on the Triangular Plane,” Math. Comput. Sci., vol. 8, no. 1, 2020.
 N. Dong and J. Roychowdhury, “Piecewise polynomial nonlinear model reduction,” Proc. - Des. Autom. Conf., pp. 484–489, 2003, doi: 10.1145/775954.775957.
 E. Holst and P. Thyregod, “A statistical test for the mean squared error,” J. Stat. Comput. Simul., vol. 63, no. 4, pp. 321–347, 1999, doi: 10.1080/00949659908811960.
 U. Khair, H. Fahmi, S. Al Hakim, and R. Rahim, “Forecasting Error Calculation with Mean Absolute Deviation and Mean Absolute Percentage Error,” J. Phys. Conf. Ser., vol. 930, no. 1, 2017, doi: 10.1088/1742-6596/930/1/012002.